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Dakota Reference Manual
Version 6.12
Explore and Predict with Confidence
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Dakota Reference Manual
Version 6.12
Explore and Predict with Confidence
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Constrained Optimization BY Linear Approximations (COBYLA)
This keyword is related to the topics:
Alias: none
Argument(s): none
Child Keywords:
| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
|---|---|---|---|---|
| Optional | initial_delta | Reasonable initial changes to optimization variables | ||
| Optional | variable_tolerance | Required or expected accuracy in optimization variables. | ||
| Optional | solution_target | Stopping criteria based on objective function value | ||
| Optional | seed | Seed of the random number generator | ||
| Optional | show_misc_options | Show algorithm parameters not exposed in Dakota input | ||
| Optional | misc_options | Set method options not available through Dakota spec | ||
| Optional | max_iterations | Number of iterations allowed for optimizers and adaptive UQ methods | ||
| Optional | convergence_tolerance | Stopping criterion based on objective function or statistics convergence | ||
| Optional | max_function_evaluations | Number of function evaluations allowed for optimizers | ||
| Optional | scaling | Turn on scaling for variables, responses, and constraints | ||
| Optional | model_pointer | Identifier for model block to be used by a method | ||
The Constrained Optimization BY Linear Approximations (COBYLA) algorithm is an extension to the Nelder-Mead simplex algorithm for handling general linear/nonlinear constraints and is invoked using the coliny_cobyla group specification. The COBYLA algorithm employs linear approximations to the objective and constraint functions, the approximations being formed by linear interpolation at N+1 points in the space of the variables. We regard these interpolation points as vertices of a simplex. The step length parameter controls the size of the simplex and it is reduced automatically from initial_delta to variable_tolerance. One advantage that COBYLA has over many of its competitors is that it treats each constraint individually when calculating a change to the variables, instead of lumping the constraints together into a single penalty function.
See the page package_scolib for important information regarding all SCOLIB methods
coliny_cobyla is inherently serial.
Stopping Critieria
COBYLA currently only supports termination based on
Other method-independent stopping criteria (max_iterations and convergence_tolerance) will be ignored if set.
Known Bugs
The implementation of the coliny_cobyla optimization method is such that the best function value is not always returned to Dakota for reporting. The user is advised to look through the Dakota screen output or the tabular output file (if generated) to confirm what the best function value and corresponding parameter values are.
The coliny_cobyla optimization method does not always respect bound constraints when scaling is turned on.
Neither bug will be fixed, as maintaining third-party source code (such as COBYLA) is outside of the Dakota project scope.
Expected HDF5 Output
If Dakota was built with HDF5 support and run with the hdf5 keyword, this method writes the following results to HDF5:
These keywords may also be of interest:
1.8.4 
